Arabian Journal for Science and Engineering最新文献

In this study, dynamic deformation behavior of electron beam melted Ti-6Al-4V alloy and effect of initial defects on deformation process of the alloy were investigated with high strain rate experimental and numerical studies. Dynamic compression tests at the strain rates of 350, 850, 1250, 1750, 1950, and 2500/s at room temperature and at higher temperatures of 150 and 240 °C were performed using a split-Hopkinson pressure bar. Compression simulations in three dimensions (3D) with LS-Dyna software were conducted using the determined Johnson–Cook parameters of the Ti-6Al-4V alloy specimens, to assess the strain, temperature distribution during deformation. In addition, simulation studies with initial defects in the model were performed to investigate the effect of these defects on strain formation during compression. The experimental results showed that strain rates over 1250/s caused failure at 45° to the loading direction. Adiabatic shear bands were observed for the specimens compressed at the strain rates of 1250/s and higher. As strain rate increased from 1250 to 2500/s, the type of adiabatic shear band altered from deformed to transformed type. The simulation results showed that initial defects in the specimen led to formation of higher plastic strain in the direction of 45° around initial defects. This high strain might be the cause of formation of adiabatic shear band. The simulation results also indicated that void morphology could affect strain distribution in the specimen.

Groundwater resources in coastal aquifers are commonly subjected to several anthropogenic threats that deteriorate their quality. This study focuses on the key factors influencing groundwater in Tarout Island, Saudi Arabia, including seawater intrusion, heavy metal contamination, and nitrate pollution. A comprehensive sampling and analysis strategy was implemented, incorporating the primary ion chemistry, stable isotopes (Oxygen-18 (δ¹⁸O), Deuterium (δ²H), Sulfur-34 (δ³⁴S_SO4)), and heavy metals/toxic elements concentrations. Both graphical and statistical tools were used to assess the presence and patterns of seawater intrusion, pollutant sources, and health hazards. According to the hydrochemical facies evolution diagram, stable isotopes and conservative ion mixing tendencies confirmed seawater mixing in 90% of tested samples. The study showed that heavy metals/toxic elements including Fe, Cr, Ni, V, As, and Co have exceeded regulation limits with 79% of groundwater samples tested being unfit for drinking purposes. In addition, nitrate was recorded surpassing the permissible limit in 38% of samples. The potential ecological risk index of the heavy metals/toxic elements varied from moderate to extremely high, with Ni, Cu, V, and As being the most hazardous metals. The results also show that 42% and 100% of tested groundwater showed potential non-carcinogenic and carcinogenic health risks, with total HQ and total CR exceeding the threshold values, respectively. The results show that seawater intrusion, heavy metal pollution, and nitrate contamination have detrimental effects on the island's groundwater quality. To mitigate the health and environmental hazards, groundwater use must be urgently managed alongside implementing suitable pollution control techniques. The study provides a robust scientific foundation for formulating policies that promote the sustainable use of Tarout Island's valuable groundwater resources. Similar anthropogenic pressures on coastal aquifers throughout the world necessitate the scientific understanding and integrated management approaches presented in this study to protect groundwater quality. It is recommended to establish a groundwater monitoring system that constantly monitors critical quality parameters to maintain an in-depth understanding of the complex nature of the vulnerable coastal aquifer.

In the present study, the influence of adding lightweight expanded clay aggregate (LECA) and natural zeolite (NZ) on the geopolymer concrete (GPC) based on ground-granulated blast furnace slag (GGBS) was investigated in terms of density, water absorption, compressive and splitting tensile strengths, and ultrasonic pulse velocity (UPV) at ambient temperature. Two sizes of LECA as the replacement of course and fine aggregates ranging from 0 to 100% were used in order to examine their suitability to produce lightweight GPC (LWGPC). NZ was utilized as a binder in the GPC with up to 15% replacement of GGBS. Test results indicated that the density of GPC decreased and water absorption value was increased with higher LECA percentage, and this behaviour is observed for NZ addition with less intensity. Compressive strength of the lightweight GPC is extremely decreased from 5 to 20% by incorporation of NZ in place of GGBS. Results indicated that LWGPC with 100% LECA compared with the specimens without LECA can effectively decrease the tensile strength by about 50% and the reduction for NZ addition is approximately 15%. The experimental results show that the variation in splitting tensile strength, compressive strength, and UPV of LWGPC is significantly influenced by binder content. A machine learning-based model was created for the mixtures produced within the scope of the experimental study and equations generated from M5p model tree that represents a strong correlation between the actual and predicted values for splitting tensile and compressive strengths with more than 99% of accuracy.

This paper investigates the pressure-driven and electroosmotic flow of Bingham plastic fluid within a curved microtube in the presence of a streaming potential. Perturbation analysis is utilised to solve the governing equations and obtain approximate analytical solutions. Validation against existing literature confirms the accuracy of the approach, with highly favourable agreement observed. The electrical double-layer (EDL) distribution is analysed for various Debye lengths, perturbation parameters, curvature ratios, and zeta potentials. As curvature increases, the EDL decreases near the lower wall and increases near the upper wall. The impact of electroosmosis force, Debye lengths, perturbation parameters, curvature ratios, and ionic Peclet number on axial velocity profiles is investigated. Axial velocity increases with the electroosmotic parameter value due to a more significant axial electric force in the inner area. Additionally, velocity decreases with increasing Bingham parameter, particularly at the lower wall region, while it increases with curvature value in the upper half of the tube. Higher flow rates are observed within curved microtubes than linear ones under similar pressure gradients and cross-sectional shapes. Increasing Debye length reduces streaming potential magnitude, favouring pressure-driven flow over electroosmotic flow. Finally, the variation of electrokinetic energy conversion efficiency with curvature ratio for different Bingham parameters is analysed. Higher Bingham parameter values increase fluid viscosity, resulting in slower fluid movement, reduced streaming potential, and decreased efficiency of electrokinetic energy conversion. This study contributes to a deeper understanding of fluid dynamics within curved microtubes and offers insights into optimising energy conversion efficiency in Bingham plastic fluid systems.

Information technology applications are crucial to the proper utilization of manufacturing equipment in the new industrial age, i.e., Industry 4.0. There are certain fundamental conditions that users must meet to adapt the manufacturing processes to Industry 4.0. For this, as in the past, there is a major need for modeling and simulation tools in this industrial age. In the creation of industry-driven predictive models for machining processes, substantial progress has recently been made. This paper includes a comprehensive review of predictive performance models for machining (particularly analytical models), as well as a list of existing models' strengths and drawbacks. It contains a review of available modeling tools, as well as their usability and/or limits in the monitoring of industrial machining operations. The goal of process models is to forecast principal variables such as stress, strain, force, and temperature. These factors, however, should be connected to performance outcomes, i.e., product quality and manufacturing efficiency, to be valuable to the industry (dimensional accuracy, surface quality, surface integrity, tool life, energy consumption, etc.). Industry adoption of cutting models depends on a model's ability to make this connection and predict the performance of process outputs. Therefore, this review article organizes and summarizes a variety of critical research themes connected to well-established analytical models for machining processes.

Lithium-ion batteries, one of the most important energy storage technologies, are widely used in portable electronic devices, electric vehicles, and energy storage systems due to their high energy density and long cycle life. However, the degradation of the batteries causes many safety hazards. The degraded batteries show some different characteristics compared with fresh ones, such as temperature distribution and evolution which can be tested and recorded by infrared (IR) thermography. This study investigates the differences in temperature distribution and average temperature evolution of degraded and fresh 18,650 Li-ion batteries at various discharging and charging currents. It is apparent that the temperature rise rate of degraded batteries is much higher than that of fresh ones in the first 30 s at a discharging current of 9 A. Also, the temperature distribution begins to change from the very beginning of the discharging, changes fast, and becomes stable before the DoD of 0.5 in the degraded batteries, while in the fresh one, it changes slowly and becomes stable after reaching a DoD of 0.5. Results show that there are obvious differences in temperature distribution and average temperature evolution between degraded and fresh batteries at high currents, while the difference at low currents is inconspicuous, which might be caused by the increase in the internal resistance in degraded batteries. Altogether, these findings suggest the potential for early detection of degraded batteries through IR thermography technology during high-current discharging.

Understanding the impact of specific design parameters is essential for enhancing pump functionality and minimizing energy consumption. Alterations in the blade exit angle impact the static pressure, relative velocity, and energy loss within the pump and associated effects on the structural behavior of the impeller. This work investigates the influence of the blade exit angle on the performance, hydraulic, and structural design of the flow passage components of the centrifugal pump. Impeller models with different exit angles (15°, 20°, 30°, 40°, and 55°) were simulated while keeping all other parameters constant. Computational investigations were conducted to examine the flow characteristics of a centrifugal pump with five distinct impellers using the shear stress transport k–ω turbulence model. The numerical investigation was validated with the previous study. The analysis focuses on variations in static pressure, relative velocity, and energy loss. Besides, the energy loss distribution was investigated to determine the total entropy generation (TEG) and entropy generation rate (EGR). The fluid–structure interaction (FSI) for several impeller models was used to examine the effect of the exit angle on the structural properties of the impeller. The results demonstrate that the blade exit angle significantly impacts the pump performance. An increase in blade exit angle has resulted in a rise in head and pressure. Furthermore, the structural behaviors, including the total deformation and equivalent stress, are discussed. The findings of this study provide valuable insights into enhancing energy efficiency and hydraulic design principles of centrifugal pumps.

The estuary of River Badung is a containment for the Nusa Dua Estuary Reservoir, which requires an assessment of sedimentation and sediment transport to determine potential reductions in its storage capacity. Therefore, this study aims to quantify and optimize the transported sediment obtained through various control measures, accompanied by the analysis of sedimentation distribution patterns in the lower reaches of River Badung. In this study, three simulation methods were used, (1) Hydrologic Engineering Center’s-River Analysis System (HEC-RAS) was applied to analyze sediment transport, (2) Aeronautical Reconnaissance Coverage Geographic Information System (ArcGIS) was implemented to process geometry data and then exported with Hydrologic Engineering Center’s-Geospatial River Analysis System (HEC-GeoRAS), and (3) Universal Soil Loss Equation (USLE) method was used to optimize sedimentation control. The results showed that River Badung had significant erosion, with sediment volume determined by USLE and HEC-RAS methods being 8040 m³ and 8740 m³, respectively. This emphasized a difference of 697 m³ or 8.66%, with the condition of sediment control structures yielding the following outputs, (1) the structures of check dams 1 and 4 required a thorough repair, (2) the capacity of check dams 2 was almost full, and (3) check dams 3 was full. Based on these results, check dams 5 and groundsill were capable of accommodating sediment, with reservoir (check dams 5) needing adjustment for appropriate functioning.

Cross-Site Scripting (XSS) attacks continue to be a significant threat to web application security, necessitating robust detection mechanisms to safeguard user data and ensure system integrity. In this study, we present a novel approach for detecting XSS attacks that harnesses the combined capabilities of the Universal Sentence Encoder (USE) and Word2Vec embeddings as a feature extractor, aiming to enhance the performance of machine learning and deep learning techniques. By leveraging the semantic understanding of sentences offered by USE and the word-level representations from Word2Vec, we obtain a comprehensive feature representation for XSS attack payloads. Our proposed approach aims to capture both fine-grained word meanings and broader sentence contexts, leading to enhanced feature extraction and improved model performance. We conducted extensive experiments utilizing machine learning and deep learning architectures to evaluate the effectiveness of our approach. The obtained results demonstrate that our combined embeddings approach outperforms traditional methods, achieving superior accuracy, precision, recall, ROC, and F1-score in detecting XSS attacks. This study not only advances XSS attack detection but also highlights the potential of state-of-the-art natural language processing techniques in web security applications. Our findings offer valuable insights for the development of more robust and effective security measures against XSS attacks.

Compared to the traditional abrasive jet polishing method, the ultrasonic coupled abrasive jet polishing (UC-AJP) method that utilizes the synergistic effects of pulse and cavitation can achieve a higher polishing efficiency. To further improve the polishing quality of the UC-AJP method, process optimization research was conducted on the quartz glass as the polishing object. The research was divided into two stages. In the first stage: the orthogonal array of the Taguchi method (TM) was used to design the experiments; the process parameters were optimized and the significant control factors were obtained through the signal-to-noise (S/N) ratio and analysis of variance (ANOVA). In the second stage: the response surface methodology (RSM) was used to establish the linear roughness prediction model and the interaction law of process parameters in the UC-AJP process was revealed, then the significant control factors were further optimized. On the basis of the above work, application research was conducted on the glass-based micro-channels with different cross-sectional structures, which verified the performance of the UC-AJP method for conformal polishing. After polishing with the optimized UC-AJP process parameters, the surface roughness of the workpiece decreases from 0.2640 to 0.0460 μm, and the surface roughness improvement rate is 82.6%.